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Despite sufficient control of volatile N-nitrosamines in foods and beverages, little attention remained on nonvolatile nitroso compounds, which are mostly unknown and relative to nitrite reactions. In a recent study, new compounds related to reactions of nitrite in beer were pyruvic acid oxime, 4-nitrosophenol, 4-cyanophenol, N-nitrosoproline ethyl ester, nitrosoguaiacol, and 2-methoxy-5-nitrophenol, as well as the already known N-nitrosoproline. The present study is intended to observe their natural occurrence in commercial beers and malts. All 22 nitrite-related products (N-products) were monitored in almost 200 samples of beers and malts. As many as 17 N-products were detected in malts, and all 22 N-products were found in beers. The hierarchical clustering grouped samples based on similarities of detected N-products by frequency of their appearance and level of response. Between N-products and N-nitrosodimethylamine concentrations in malts, only moderate Pearson correlations were found. The same applied to N-product correlations with the apparent total nitroso compound determination in beers.

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Selection of the appropriate yeast strain is one of the most crucial steps in a brewery. Traditionally, yeast strain's abilities during beer fermentation are described according to brewer's experiences. Hence, these descriptions could be inaccurate and strictly based on sensory experiences. In this study, lager beers fermented by four traditional bottom-fermented yeast strains were characterized in detail by sensomic approach. The obtained results revealed that yeast strains can influence most of the sensory-related components in beer, not only esters and higher alcohols, but also carbonyls, amino acids, saccharides, fatty acids, heterocyclic compounds, hop oils, and other hop-related components. By comparison of chemical and sensory characteristics of each studied beer, the theoretical importance of sensory interactions on beer flavor perception was also revealed as the general conception that the beers with similar flavors have also similar chemical profiles (and vice versa) was seemed as not valid.

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The effect of nitrites in foods and beverages still raises discussion due to the possible formation of harmful nitroso compounds. However, as most of these compounds in beer were not structurally characterized yet, the research about their toxicological relevance for consumers is limited. This study is focused on identification of the products formed by nitrite (or isotopically labeled nitrite 15N) reactions in beer using gas chromatography with tandem mass spectrometry. In total, 19 products were identified, and some of them were structurally characterized and confirmed by comparing retention indices and mass spectra of standard/synthesized compounds. Identified compounds were representatives of nitroso, nitro, oxime, and even cyano compounds. For the peaks which were not structurally identified, primary structural characteristics were also listed. Found products were further screened in 16 authentic beer samples which showed the apparent occurrence of found compounds in non-treated beers.

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Retention index in gas chromatographic analyses is an essential tool for appropriate analyte identification. Currently, many libraries providing retention indices for a huge number of compounds on distinct stationary phase chemistries are available. However, situation could be complicated in the case of unknown unknowns not present in such libraries. The importance of identification of these compounds have risen together with a rapidly expanding interest in non-targeted analyses in the last decade. Therefore, precise in silico computation/prediction of retention indices based on a suggested molecular structure will be highly appreciated in such situations. On this basis, a predictive model based on deep learning was developed and presented in this paper. It is designed for user-friendly and accurate prediction of retention indices of compounds in gas chromatography with the semi-standard non-polar stationary phase. Simplified Molecular Input Entry System (SMILES) is used as the model's input. Architecture of the model consists of 2D-convolutional layers, together with batch normalization, max pooling, dropout, and three residual connections. The model reaches median absolute error of prediction of the retention index for validation and test set at 16.4 and 16.0 units, respectively. Median percentage error is lower than or equal to 0.81% in the case of all mentioned data sets. Finally, the DeepReI model is presented in R package, and is available on https://github.com/TomasVrzal/DeepReI together with a user-friendly graphical user interface.

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The problems of contamination of many products by nitroso compounds have been discussed since 1970's and have been partially solved, namely, the contamination by carcinogenic volatile N-nitrosamines. However, there is still a gap in knowing non-volatile nitroso compounds in terms of both the determination of these compounds and the description of their toxicity. Therefore, a procedure for their detailed non-targeted study is necessary to be developed. Based on these facts, a new method permitting the detection and the classification of nitroso compound groups, such as N-nitroso, C-nitroso, and interfering substances in the nitrosamine specific chemiluminescence detection after previous gas chromatographic separation, was developed. The method is based on signal profiling of chromatographic peaks recorded by a chemiluminescence detector at different pyrolytic temperatures and subsequent multivariate chemometric classification. The resulting classification function by linear discriminant analysis shows good performance with total accuracy of 96.12% after the method validation. The method was successfully applied and demonstrated on a non-targeted beer sample analysis. Nitroso compounds detected by the method were selected for detailed structural analysis by GC-MS/MS. The combination of the presented method with the MS/MS instrumentation provides a really powerful analytical tool for the identification of unknown nitroso compounds in complex samples. This study represents a valuable contribution to the protocols of identification of organic compounds with the nitrogen functional groups - toxicologically and analytically important nitroso compounds.

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BACKGROUND: Although fatty acids have a beneficial effect on yeast growth during fermentation, their effect on foam and sensory stability of beer is negative. In general, long-chain fatty acids originate from raw materials, whereas short-chain acids are produced by yeast during fermentation. If the concentration of short-chain fatty acids, especially isovaleric and butyric acid, overreaches a sensory threshold, then an unpleasant aroma, such as cheesy or sweaty feet, can be formed in beer. RESULTS: The distribution of fatty acids, from the preparation of sweet wort to the final beer, was studied using chemometric evaluation. Differences were observed between the decoction and infusion system using four barley varieties. Attention was paid to the behavior of short-chain fatty acids, namely isovaleric acid. The concentration of isovaleric acid in commercial beers brewed in infusion and decoction systems was approximately 1.4 and 1.0 mg L-1 , respectively. The same trend was observed in experimental samples (1.3 and 0.5 mg L-1 , respectively). This phenomenon was confirmed experimentally; based on the results, this possibly explains why, during the fermentation, isovaleric acid is coupled with the redox state of yeast cell, which is given by the wort composition (i.e. by the mashing process). CONCLUSION: The formation of isovaleric acid is not only caused by microbiology infection or by oxidized hops, but also is influenced by the mashing process. © 2018 Society of Chemical Industry.

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